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Machine Learning and Order Book Dynamics

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Standard price-based techniques for predicting future movements of financial assets have low performance over short predictive horizons. Information relating to the interaction of buyers and sellers on exchanges has greater prognostic ability over these time scales. With this in mind, we use the volumes on a EURUSD limit order book to construct simple features. We then take traditional models of market microstructure and incorporate them into the machine learning framework using Fisher kernels. These Fisher kernels are combined with kernels constructed from the simple volume-based features and price-based indicators using Multiple Kernel Learning. Outperformance relative to a benchmark is found for subsets of these kernels.

This talk is part of the Machine Learning @ CUED series.

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